3 papers
cs.AI2026
MolBioKG: Grounding Out-of-Graph Molecules in Biomedical Knowledge Graphs via Multi-Resolution Structural Anchoring
Yiming Zhang, Hikaru Shindo, Shuan Chen +5
Biomedical knowledge graphs (KGs) accelerate drug discovery, but standard pipelines assume query molecules already exist as graph entities, leaving unregistered molecules disconnec…
cond-mat.mtrl-sci2026
LLM-guided phase diagram construction through high-throughput experimentation
Ryo Tamura, Haruhiko Morito, Yuna Oikawa +7
Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate whether large language models (…
cond-mat.mtrl-sci2025
aLLoyM: A large language model for alloy phase diagram prediction
Yuna Oikawa, Guillaume Deffrennes, Taichi Abe +2
Large Language Models (LLMs) are general-purpose tools with wide-ranging applications, including in materials science. In this work, we introduce aLLoyM, a fine-tuned LLM specifica…